A Parallel Apriori Algorithm for Frequent Itemsets Mining

Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated. One of the implementations optimizing the da...

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Bibliographic Details
Published inFourth International Conference on Software Engineering Research, Management, and Applications : SERA 2006 : proceedings : 9-11 August, 2006 : Seattle, Washington pp. 87 - 94
Main Authors Yanbin Ye, Chia-Chu Chiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Application software
Apriori
Association rules
Computer science
Concurrent computing
Data mining
Data structures
Drives
Frequent Itemsets Mining (FIM)
Itemsets
Parallel Computing
Parallel processing
Transaction databases
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Summary:Finding frequent itemsets is one of the most investigated fields of data mining. The Apriori algorithm is the most established algorithm for frequent itemsets mining (FIM). Several implementations of the Apriori algorithm have been reported and evaluated. One of the implementations optimizing the data structure with a trie by Bodon catches our attention. The results of the Bodon's implementation for finding frequent itemsets appear to be faster than the ones by Borgelt and Goethals. In this paper, we revised Bodon's implementation into a parallel one where input transactions are read by a parallel computer. The effect a parallel computer on this modified implementation is presented
ISBN:076952656X
9780769526560
DOI:10.1109/SERA.2006.6